Yuliang Zou

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 1,069 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #12,314 of 21,575 by trust here, #4,690 by citations
• Artificial Intelligence — 104 citations into it; #26,355 of 35,345 by trust here, #12,580 by citations
• Representation & Self-Supervised Learning — 51 citations into it
• Generative Models — 21 citations into it
• Model Architectures — 19 citations into it
• Media Technology — 18 citations into it; #3,355 of 3,621 by trust here, #1,923 by citations
• Aerospace Engineering — 17 citations into it
• Evaluation & Benchmarks — 15 citations into it

Whom Yuliang Zou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Karen Simonyan
7. Yoshua Bengio
8. Yann LeCun
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Yuliang Zou overall

1. Lihe Yang
2. Mathieu Pagé Fortin
3. Yun-Chun Chen
4. Vitor Campanholo Guizilini
5. Chong Zhou
6. Xuan Luo
7. Anurag Ranjan
8. Jiawang Bian
9. Yanbei Chen
10. Xiaokang Chen

Papers from Yuliang Zou's perspective

Ordered by how strongly the researchers Yuliang Zou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning to Generate Long-term Future via Hierarchical Prediction (2017) — 400 citations
• Label Efficient Learning of Transferable Representations acrosss Domains and Tasks (2017) — 290 citations
• DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency (2018) — 504 citations
• iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection (2018) — 335 citations
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation (2020) — 376 citations